Machine Learning: MCQs Set – 23

Q221: The k-means algorithm is a

  • (A) Supervised learning algorithm
  • (B) Unsupervised learning algorithm
  • (C) Semi-supervised learning algorithm
  • (D) Weakly supervised learning algorithm

Q222: When the number of features increase

  • (A) Computation time increases
  • (B) Model becomes complex
  • (C) Learning accuracy decreases
  • (D) All of the above

Q223: For unsupervised learning we have ____ model.

  • (A) interactive
  • (B) predictive
  • (C) descriptive
  • (D) prescriptive

Q224: Engineering a good feature space is a crucial ___ for the success of any machine learning model.

  • (A) Pre-requisite
  • (B) Process
  • (C) Objective
  • (D) None of the above

Q225: In LDA, intra-class and inter-class ___ matrices are calculated.

  • (A) Scatter
  • (B) Adjacency
  • (C) Similarity
  • (D) None of the above

Q226: We can define this probability as p(A|B) = p(A,B)/p(B) if p(B) > 0

  • (A) Conditional probability
  • (B) Marginal probability
  • (C) Bayes probability
  • (D) Normal probability

Q227: Predicting whether a tumour is malignant or benign is an example of?

  • (A) Unsupervised Learning
  • (B) Supervised Regression Problem
  • (C) Supervised Classification Problem
  • (D) Categorical Attribute

Q228: This refers to the transformations applied to the identified data before feeding the same into the algorithm.

  • (A) Problem Identification
  • (B) Identification of Required Data
  • (C) Data Pre-processing
  • (D) Definition of Training Data Set

Q229: Which of the following is true about SVM?

  • (A) It is useful only in high-dimensional spaces
  • (B) It requires less memory
  • (C) SVM does not perform well when we have a large data set
  • (D) SVM performs well when we have a large data set

Q230: When you find many noises in data, which of the following options would you consider in kNN?

  • (A) Increase the value of k
  • (B) Decrease the value of k
  • (C) Noise does not depend on k
  • (D) k = 0



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